152 research outputs found
The impact of different sentiment in investment decisions: evidence from China’s stock markets IPOs
In this study, we used data on China’s initial public offerings (IPOs),
market volatility and macro environment before and after two
stock crashes during 2006–2016 to investigate how different
investor sentiment affects IPO first-day flipping. The empirical
results show that the expected returns of allocated investors are
affected by sentiment, with allocated investors having higher psychological
expectations of future returns during an optimistic bull
market and their optimism discouraging first-day flipping, while
higher risk-free interest rate levels and rising broad market indices
also discourage first-day flipping and tend to sell in the future. The
pessimistic bear market during which allocated investors have
lower psychological expectations of future returns, their pessimism
will promote first-day flipping, and the increase in the risk-free rate
level will also promote first-day flipping, which is the opposite of
the optimistic bull market, indicating that their risk aversion has
increased and they tend to sell on the same day. We also found an
anomaly that the greater the decline in the broad market index
during a pessimistic bear market, the more inclined the allocated
investors are to sell in the future when the broad market index rises
in an attempt to gain higher returns. These findings help explain
and understand the impact of market and macro index fluctuations
on investor behavior under different investor sentiments
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
3D anomaly detection is an emerging and vital computer vision task in
industrial manufacturing (IM). Recently many advanced algorithms have been
published, but most of them cannot meet the needs of IM. There are several
disadvantages: i) difficult to deploy on production lines since their
algorithms heavily rely on large pre-trained models; ii) hugely increase
storage overhead due to overuse of memory banks; iii) the inference speed
cannot be achieved in real-time. To overcome these issues, we propose an easy
and deployment-friendly network (called EasyNet) without using pre-trained
models and memory banks: firstly, we design a multi-scale multi-modality
feature encoder-decoder to accurately reconstruct the segmentation maps of
anomalous regions and encourage the interaction between RGB images and depth
images; secondly, we adopt a multi-modality anomaly segmentation network to
achieve a precise anomaly map; thirdly, we propose an attention-based
information entropy fusion module for feature fusion during inference, making
it suitable for real-time deployment. Extensive experiments show that EasyNet
achieves an anomaly detection AUROC of 92.6% without using pre-trained models
and memory banks. In addition, EasyNet is faster than existing methods, with a
high frame rate of 94.55 FPS on a Tesla V100 GPU
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding
(KGE), which attempts to learn the embeddings of the entities and relations as
numerical vectors and mathematical mappings via machine learning (ML). However,
there has been limited research that applies KGE for industrial problems in
manufacturing. This paper investigates whether and to what extent KGE can be
used for an important problem: quality monitoring for welding in manufacturing
industry, which is an impactful process accounting for production of millions
of cars annually. The work is in line with Bosch research of data-driven
solutions that intends to replace the traditional way of destroying cars, which
is extremely costly and produces waste. The paper tackles two very challenging
questions simultaneously: how large the welding spot diameter is; and to which
car body the welded spot belongs to. The problem setting is difficult for
traditional ML because there exist a high number of car bodies that should be
assigned as class labels. We formulate the problem as link prediction, and
experimented popular KGE methods on real industry data, with consideration of
literals. Our results reveal both limitations and promising aspects of adapted
KGE methods.Comment: Paper accepted at ISWC2023 In-Use trac
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